| Project/Area Number |
19K20942
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| Project/Area Number (Other) |
18H05746 (2018)
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| Research Category |
Grant-in-Aid for Research Activity Start-up
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| Allocation Type | Multi-year Fund (2019)
Single-year Grants (2018) |
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| Review Section |
:Education and related fields
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| Research Institution | Kyoto University |
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Principal Investigator |
Majumdar Rwito 京都大学, 学術情報メディアセンター, 特定研究員 (30823348)
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| Project Period (FY) |
2018-08-24 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥2,990,000 (Direct Cost: ¥2,300,000、Indirect Cost: ¥690,000)
Fiscal Year 2019: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2018: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | GOAL / Self-directed Learning / Learning Analytics / DAPER model / Quantified Self / Wearable trackers / Self Direction Skills / GOAL system / Self-directedness / Self-direction skills |
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| Outline of Research at the Start |
For the 21st century learner, self-direction is a crucial skill. This proposal outlines an interdisciplinary data driven approach to support acquisition of Self-Direction Skill. It envisions to design and develop Goal Oriented Active Learner (GOAL) system to synchronize-visualize-analyze multisource data regarding student’s learning and physical activities. Synchronous logging of learner’s learning and physical activities shall generate a data poll that potentially opens up further research opportunities at the intersection of LA (Learning Analytics) and QS (Quantified-Self) research.
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| Outline of Final Research Achievements |
GOAL project achieved to build a platform to collect learning and physical activity data of learners. Based on the user's reading behaviours and physical activity logs the system supported a DAPER (data collection - analysis - planning - execution monitoring - reflection) model to train for self-direction skills. Our need analysis highlighted the necessity of improving self-direction skills in learners both at school and university levels. The features required in the technology platform was refined over two design iterations. The initial data collected is a first of its kind dataset of learner's attribute from beyond daily learning activity context. Based on that we proposed data-driven models to measure their analysis and planning skills. It was also used to understand students self-directed learning behaviours.
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| Academic Significance and Societal Importance of the Research Achievements |
The GOAL introduced a data-driven approach for measuring and adaptively supporting self-directed skills following the proposed DAPER process. The technical foundation paves way for further research using student's learning and physical activity data.
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